Automotive Security Analyzer for Exploitability Risks: An Automated and Attack Graph-Based Evaluation of On-Board Networks
Salfer, Martin
Our lives depend on automotive cybersecurity, protecting us inside and near vehicles. If vehicles go rogue, they can operate against the driver’s will and potentially drive off a cliff or into a crowd. The “Automotive Security Analyzer for Exploitability Risks” (AutoSAlfER) evaluates the exploitability risks of automotive on-board networks by attack graphs. AutoSAlfER’s Multi-Path Attack Graph algorithm is 40 to 200 times smaller in RAM and 200 to 5 000 times faster than a comparable implementation using Bayesian networks, and the Single-Path Attack Graph algorithm constructs the most reasonable attack path per asset with a computational, asymptotic complexity of only O(n * log(n)), instead of O(n2). AutoSAlfER runs on a self-written graph database, heuristics, pruning, and homogenized Gaussian distributions and boosts people’s productivity for a more sustainable and secure automotive on-board network. Ultimately, we enjoy more safety and security in and around autonomous, connected, electrified, and shared vehicles.
Categories:
Year:
2024
Edition:
1
Publisher:
Springer Vieweg
Language:
english
Pages:
268
ISBN 10:
3658435054
ISBN 13:
9783658435059
File:
PDF, 4.45 MB
IPFS:
,
english, 2024
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